Overview

Dataset statistics

Number of variables10
Number of observations518544
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.5 MiB
Average record size in memory88.0 B

Variable types

Text2
DateTime1
Numeric7

Alerts

active_cases is highly overall correlated with confirmed and 2 other fieldsHigh correlation
case_fatality_ratio is highly overall correlated with deaths and 1 other fieldsHigh correlation
confirmed is highly overall correlated with active_cases and 1 other fieldsHigh correlation
confirmed_diff is highly overall correlated with active_casesHigh correlation
deaths is highly overall correlated with active_cases and 2 other fieldsHigh correlation
incident_rate is highly overall correlated with case_fatality_ratioHigh correlation
deaths is highly skewed (γ1 = 22.69199042)Skewed
recovered is highly skewed (γ1 = 51.89491702)Skewed
case_fatality_ratio is highly skewed (γ1 = 171.1976506)Skewed
active_cases is highly skewed (γ1 = -61.27974409)Skewed
confirmed has 10645 (2.1%) zerosZeros
deaths has 164096 (31.6%) zerosZeros
recovered has 442505 (85.3%) zerosZeros
incident_rate has 161809 (31.2%) zerosZeros
case_fatality_ratio has 260604 (50.3%) zerosZeros
active_cases has 15242 (2.9%) zerosZeros
confirmed_diff has 14565 (2.8%) zerosZeros

Reproduction

Analysis started2025-11-25 20:49:24.576508
Analysis finished2025-11-25 20:49:42.508181
Duration17.93 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Distinct738
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
2025-11-25T20:49:42.721962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length44
Median length40
Mean length8.4130797
Min length2

Characters and Unicode

Total characters4362552
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)< 0.1%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown
ValueCountFrequency (%)
unknown32784
 
5.3%
texas29528
 
4.8%
virginia24673
 
4.0%
georgia22878
 
3.7%
carolina21572
 
3.5%
north20485
 
3.3%
new19878
 
3.2%
kentucky14798
 
2.4%
south14781
 
2.4%
missouri14023
 
2.3%
Other values (822)399246
65.0%
2025-11-25T20:49:43.068763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a551403
 
12.6%
n442268
 
10.1%
i435045
 
10.0%
o352823
 
8.1%
s314174
 
7.2%
e247521
 
5.7%
r220247
 
5.0%
l147008
 
3.4%
t136755
 
3.1%
k111674
 
2.6%
Other values (50)1403634
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)4362552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a551403
 
12.6%
n442268
 
10.1%
i435045
 
10.0%
o352823
 
8.1%
s314174
 
7.2%
e247521
 
5.7%
r220247
 
5.0%
l147008
 
3.4%
t136755
 
3.1%
k111674
 
2.6%
Other values (50)1403634
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4362552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a551403
 
12.6%
n442268
 
10.1%
i435045
 
10.0%
o352823
 
8.1%
s314174
 
7.2%
e247521
 
5.7%
r220247
 
5.0%
l147008
 
3.4%
t136755
 
3.1%
k111674
 
2.6%
Other values (50)1403634
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4362552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a551403
 
12.6%
n442268
 
10.1%
i435045
 
10.0%
o352823
 
8.1%
s314174
 
7.2%
e247521
 
5.7%
r220247
 
5.0%
l147008
 
3.4%
t136755
 
3.1%
k111674
 
2.6%
Other values (50)1403634
32.2%
Distinct243
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
2025-11-25T20:49:43.380379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length13
Mean length11.998099
Min length4

Characters and Unicode

Total characters6221542
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan
ValueCountFrequency (%)
united430919
44.9%
states427494
44.6%
russia7723
 
0.8%
china5323
 
0.6%
japan4741
 
0.5%
mexico3403
 
0.4%
colombia3280
 
0.3%
kingdom3247
 
0.3%
malaysia3128
 
0.3%
india3114
 
0.3%
Other values (269)66336
 
6.9%
2025-11-25T20:49:43.858074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t1301300
20.9%
e897379
14.4%
a534744
8.6%
i495062
 
8.0%
n483856
 
7.8%
s457059
 
7.3%
d451048
 
7.2%
440164
 
7.1%
S436442
 
7.0%
U434170
 
7.0%
Other values (51)290318
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)6221542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t1301300
20.9%
e897379
14.4%
a534744
8.6%
i495062
 
8.0%
n483856
 
7.8%
s457059
 
7.3%
d451048
 
7.2%
440164
 
7.1%
S436442
 
7.0%
U434170
 
7.0%
Other values (51)290318
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6221542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t1301300
20.9%
e897379
14.4%
a534744
8.6%
i495062
 
8.0%
n483856
 
7.8%
s457059
 
7.3%
d451048
 
7.2%
440164
 
7.1%
S436442
 
7.0%
U434170
 
7.0%
Other values (51)290318
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6221542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t1301300
20.9%
e897379
14.4%
a534744
8.6%
i495062
 
8.0%
n483856
 
7.8%
s457059
 
7.3%
d451048
 
7.2%
440164
 
7.1%
S436442
 
7.0%
U434170
 
7.0%
Other values (51)290318
 
4.7%
Distinct200
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.9 MiB
Minimum2020-02-01 00:00:00
Maximum2021-04-02 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T20:49:44.045141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:44.246333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

confirmed
Real number (ℝ)

High correlation  Zeros 

Distinct28505
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3024.1744
Minimum0
Maximum804342
Zeros10645
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2025-11-25T20:49:44.446193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q124
median118
Q3650
95-th percentile9758.85
Maximum804342
Range804342
Interquartile range (IQR)626

Descriptive statistics

Standard deviation18393.475
Coefficient of variation (CV)6.0821475
Kurtosis385.09267
Mean3024.1744
Median Absolute Deviation (MAD)111
Skewness16.017548
Sum1.5681675 × 109
Variance3.3831991 × 108
MonotonicityNot monotonic
2025-11-25T20:49:44.642364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010645
 
2.1%
18731
 
1.7%
27592
 
1.5%
37411
 
1.4%
56998
 
1.3%
46861
 
1.3%
66423
 
1.2%
75638
 
1.1%
85635
 
1.1%
95022
 
1.0%
Other values (28495)447588
86.3%
ValueCountFrequency (%)
010645
2.1%
18731
1.7%
27592
1.5%
37411
1.4%
46861
1.3%
56998
1.3%
66423
1.2%
75638
1.1%
85635
1.1%
95022
1.0%
ValueCountFrequency (%)
8043421
< 0.1%
8034041
< 0.1%
8014221
< 0.1%
7962091
< 0.1%
7925411
< 0.1%
7844531
< 0.1%
7806891
< 0.1%
7761351
< 0.1%
7656701
< 0.1%
7642811
< 0.1%

deaths
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct6176
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.70086
Minimum0
Maximum50127
Zeros164096
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2025-11-25T20:49:44.853981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q317
95-th percentile375
Maximum50127
Range50127
Interquartile range (IQR)17

Descriptive statistics

Standard deviation1253.9749
Coefficient of variation (CV)8.6659804
Kurtosis666.92731
Mean144.70086
Median Absolute Deviation (MAD)2
Skewness22.69199
Sum75033765
Variance1572452.9
MonotonicityNot monotonic
2025-11-25T20:49:45.038869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0164096
31.6%
164562
 
12.5%
236387
 
7.0%
323877
 
4.6%
416618
 
3.2%
512318
 
2.4%
611270
 
2.2%
79201
 
1.8%
87842
 
1.5%
96966
 
1.3%
Other values (6166)165407
31.9%
ValueCountFrequency (%)
0164096
31.6%
164562
 
12.5%
236387
 
7.0%
323877
 
4.6%
416618
 
3.2%
512318
 
2.4%
611270
 
2.2%
79201
 
1.8%
87842
 
1.5%
96966
 
1.3%
ValueCountFrequency (%)
501271
< 0.1%
501111
< 0.1%
501031
< 0.1%
500951
< 0.1%
500841
< 0.1%
500731
< 0.1%
500581
< 0.1%
500391
< 0.1%
500311
< 0.1%
500151
< 0.1%

recovered
Real number (ℝ)

Skewed  Zeros 

Distinct17625
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1653.3306
Minimum0
Maximum2184825
Zeros442505
Zeros (%)85.3%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2025-11-25T20:49:45.220057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2671
Maximum2184825
Range2184825
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21617.377
Coefficient of variation (CV)13.075048
Kurtosis3916.724
Mean1653.3306
Median Absolute Deviation (MAD)0
Skewness51.894917
Sum8.5732468 × 108
Variance4.6731099 × 108
MonotonicityNot monotonic
2025-11-25T20:49:45.408733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0442505
85.3%
11123
 
0.2%
2627
 
0.1%
16505
 
0.1%
18494
 
0.1%
3482
 
0.1%
13472
 
0.1%
8439
 
0.1%
11430
 
0.1%
4412
 
0.1%
Other values (17615)71055
 
13.7%
ValueCountFrequency (%)
0442505
85.3%
11123
 
0.2%
2627
 
0.1%
3482
 
0.1%
4412
 
0.1%
5404
 
0.1%
6395
 
0.1%
7380
 
0.1%
8439
 
0.1%
9219
 
< 0.1%
ValueCountFrequency (%)
21848251
< 0.1%
21539391
< 0.1%
21406141
< 0.1%
21183671
< 0.1%
21013261
< 0.1%
20844651
< 0.1%
20536991
< 0.1%
20207741
< 0.1%
19977611
< 0.1%
19854841
< 0.1%

incident_rate
Real number (ℝ)

High correlation  Zeros 

Distinct228887
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean485.43393
Minimum0
Maximum14338.887
Zeros161809
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2025-11-25T20:49:45.607129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median159.03057
Q3605.68657
95-th percentile2088.1374
Maximum14338.887
Range14338.887
Interquartile range (IQR)605.68657

Descriptive statistics

Standard deviation845.38953
Coefficient of variation (CV)1.7415131
Kurtosis31.804132
Mean485.43393
Median Absolute Deviation (MAD)159.03057
Skewness4.1702036
Sum2.5171885 × 108
Variance714683.47
MonotonicityNot monotonic
2025-11-25T20:49:45.821173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0161809
31.2%
365.8536585102
 
< 0.1%
177.1479185101
 
< 0.1%
365.40803999
 
< 0.1%
84.8896434698
 
< 0.1%
373.241458596
 
< 0.1%
19.9973336995
 
< 0.1%
395.14535795
 
< 0.1%
1.62386685195
 
< 0.1%
10.9880450195
 
< 0.1%
Other values (228877)355859
68.6%
ValueCountFrequency (%)
0161809
31.2%
0.0274115292995
 
< 0.1%
0.0894151766824
 
< 0.1%
0.093359698115
 
< 0.1%
0.10059207382
 
< 0.1%
0.11176897092
 
< 0.1%
0.122945867921
 
< 0.1%
0.16302363191
 
< 0.1%
0.16765345631
 
< 0.1%
0.17883035342
 
< 0.1%
ValueCountFrequency (%)
14338.886922
 
< 0.1%
14330.024811
 
< 0.1%
14295.891711
 
< 0.1%
14272.144381
 
< 0.1%
14205.955331
 
< 0.1%
14143.92061
 
< 0.1%
14117.786751
 
< 0.1%
14108.472175
< 0.1%
14099.610071
 
< 0.1%
14081.885861
 
< 0.1%

case_fatality_ratio
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct84336
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.143644
Minimum0
Maximum5500
Zeros260604
Zeros (%)50.3%
Negative0
Negative (%)0.0%
Memory size7.9 MiB
2025-11-25T20:49:46.305505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.5773196
95-th percentile8.9309353
Maximum5500
Range5500
Interquartile range (IQR)2.5773196

Descriptive statistics

Standard deviation21.191792
Coefficient of variation (CV)9.885873
Kurtosis35509.126
Mean2.143644
Median Absolute Deviation (MAD)0
Skewness171.19765
Sum1111573.7
Variance449.09206
MonotonicityNot monotonic
2025-11-25T20:49:46.435325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0260604
50.3%
7.142857143925
 
0.2%
3.703703704879
 
0.2%
5.555555556873
 
0.2%
6.25872
 
0.2%
8.333333333870
 
0.2%
5.882352941866
 
0.2%
4.761904762856
 
0.2%
4.166666667839
 
0.2%
4.347826087837
 
0.2%
Other values (84326)250123
48.2%
ValueCountFrequency (%)
0260604
50.3%
0.047525170741
 
< 0.1%
0.04755949341
 
< 0.1%
0.047604774581
 
< 0.1%
0.047647619381
 
< 0.1%
0.047726790641
 
< 0.1%
0.047791839991
 
< 0.1%
0.047842650841
 
< 0.1%
0.047868945461
 
< 0.1%
0.047912267311
 
< 0.1%
ValueCountFrequency (%)
55001
< 0.1%
54002
< 0.1%
3766.6666671
< 0.1%
3733.3333331
< 0.1%
3666.6666671
< 0.1%
36001
< 0.1%
27502
< 0.1%
27002
< 0.1%
24001
< 0.1%
22001
< 0.1%

active_cases
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct22370
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1226.1429
Minimum-2184825
Maximum290073
Zeros15242
Zeros (%)2.9%
Negative3027
Negative (%)0.6%
Memory size7.9 MiB
2025-11-25T20:49:46.563937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2184825
5-th percentile1
Q120
median96
Q3488
95-th percentile5718.7
Maximum290073
Range2474898
Interquartile range (IQR)468

Descriptive statistics

Standard deviation19859.44
Coefficient of variation (CV)16.196677
Kurtosis5463.2763
Mean1226.1429
Median Absolute Deviation (MAD)91
Skewness-61.279744
Sum6.3580903 × 108
Variance3.9439735 × 108
MonotonicityNot monotonic
2025-11-25T20:49:46.692441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015242
 
2.9%
110306
 
2.0%
28972
 
1.7%
38736
 
1.7%
47823
 
1.5%
57518
 
1.4%
66998
 
1.3%
76231
 
1.2%
85898
 
1.1%
95219
 
1.0%
Other values (22360)435601
84.0%
ValueCountFrequency (%)
-21848251
< 0.1%
-21539391
< 0.1%
-21406141
< 0.1%
-21183671
< 0.1%
-21013261
< 0.1%
-20844651
< 0.1%
-20536991
< 0.1%
-20207741
< 0.1%
-19977611
< 0.1%
-19854841
< 0.1%
ValueCountFrequency (%)
2900731
< 0.1%
2890701
< 0.1%
2873291
< 0.1%
2861241
< 0.1%
2847981
< 0.1%
2832411
< 0.1%
2815911
< 0.1%
2797861
< 0.1%
2779151
< 0.1%
2762621
< 0.1%

confirmed_diff
Real number (ℝ)

High correlation  Zeros 

Distinct40037
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.14442
Minimum-786289
Maximum728576
Zeros14565
Zeros (%)2.8%
Negative243306
Negative (%)46.9%
Memory size7.9 MiB
2025-11-25T20:49:46.830261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-786289
5-th percentile-4239.85
Q1-176
median1
Q3193
95-th percentile4391.85
Maximum728576
Range1514865
Interquartile range (IQR)369

Descriptive statistics

Standard deviation18734.405
Coefficient of variation (CV)1425.2744
Kurtosis366.21016
Mean13.14442
Median Absolute Deviation (MAD)184
Skewness-0.69446413
Sum6815960
Variance3.5097793 × 108
MonotonicityNot monotonic
2025-11-25T20:49:46.961385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014565
 
2.8%
13842
 
0.7%
23291
 
0.6%
-13176
 
0.6%
33043
 
0.6%
42630
 
0.5%
-22623
 
0.5%
52368
 
0.5%
62356
 
0.5%
-32343
 
0.5%
Other values (40027)478307
92.2%
ValueCountFrequency (%)
-7862891
< 0.1%
-7745771
< 0.1%
-7583211
< 0.1%
-7421521
< 0.1%
-7318141
< 0.1%
-7309871
< 0.1%
-7291421
< 0.1%
-7278431
< 0.1%
-7241701
< 0.1%
-7131261
< 0.1%
ValueCountFrequency (%)
7285761
< 0.1%
7182561
< 0.1%
7034061
< 0.1%
6885621
< 0.1%
6753871
< 0.1%
6618471
< 0.1%
6565401
< 0.1%
6550181
< 0.1%
6545171
< 0.1%
6480231
< 0.1%

Interactions

2025-11-25T20:49:39.941697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:32.288640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:33.822306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:35.042075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:36.173186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:37.408398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:38.585903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:40.123623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:32.507463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:33.965378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:35.205874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:36.364703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:37.581846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:38.758145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:40.296968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:32.711914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:34.109267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:35.374852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:36.543072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:37.742643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:38.926923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:40.467270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:32.925628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:34.264704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:35.524365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:36.708064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:37.902140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:39.088963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:40.657562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:33.148303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:34.569639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:35.682240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:36.878620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:38.065888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:39.437083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:40.827811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:33.397850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:34.724949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:35.840153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:37.048102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:38.226666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:39.609144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:40.990526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:33.616928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:34.877329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:35.997878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:37.219410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:38.392200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T20:49:39.770331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-25T20:49:47.059912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
active_casescase_fatality_ratioconfirmedconfirmed_diffdeathsincident_raterecovered
active_cases1.0000.4580.9390.5010.7930.4990.156
case_fatality_ratio0.4581.0000.4990.1810.6340.6180.137
confirmed0.9390.4991.0000.4980.8500.4590.379
confirmed_diff0.5010.1810.4981.0000.4550.1470.075
deaths0.7930.6340.8500.4551.0000.3360.329
incident_rate0.4990.6180.4590.1470.3361.000-0.091
recovered0.1560.1370.3790.0750.329-0.0911.000

Missing values

2025-11-25T20:49:41.214165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T20:49:41.690214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

province_statecountry_regionlast_updateconfirmeddeathsrecoveredincident_ratecase_fatality_ratioactive_casesconfirmed_diff
91UnknownAfghanistan2020-02-245000.00.050.0
1316UnknownAfghanistan2020-02-248000.00.083.0
1548UnknownAfghanistan2020-03-088000.00.080.0
2148UnknownAfghanistan2020-03-108000.00.080.0
2394UnknownAfghanistan2020-03-1111000.00.0113.0
3183UnknownAfghanistan2020-03-1414000.00.0143.0
3464UnknownAfghanistan2020-03-1520000.00.0206.0
3758UnknownAfghanistan2020-03-1625010.00.0245.0
4081UnknownAfghanistan2020-03-1726010.00.0251.0
5061UnknownAfghanistan2020-03-2024010.00.023-2.0
province_statecountry_regionlast_updateconfirmeddeathsrecoveredincident_ratecase_fatality_ratioactive_casesconfirmed_diff
563064UnknownZimbabwe2020-08-256070155495040.8398702.553542965140.0
567050UnknownZimbabwe2020-08-266196166496141.6876162.6791481069126.0
571036UnknownZimbabwe2020-08-276251179500142.0576652.863542107155.0
575022UnknownZimbabwe2020-08-286292189501042.3335193.003814109341.0
579008UnknownZimbabwe2020-08-296388195504342.9794213.052599115096.0
582994UnknownZimbabwe2020-08-306406196505643.1005283.059632115418.0
586980UnknownZimbabwe2020-08-316412196506143.1408973.05676911556.0
590970UnknownZimbabwe2020-09-016497202522143.7127903.109127107485.0
2097Unknownoccupied Palestinian territory2020-03-1025000.0000000.000000250.0
2475Unknownoccupied Palestinian territory2020-03-110000.0000000.0000000-25.0